Abstract
ABSTRACT: The objective of this study was to compare non-linear models fitted to the growth curves of quail to determine which model best describes their growth and check the similarity between models by analyzing parameter estimates.Weight and age data of meat-type European quail (Coturnix coturnix coturnix) of three lines were used, from an experiment in a 2 × 4 factorial arrangement in a completely randomized design, consisting of two metabolizable energy levels, four crude protein levels and six replicates. The non-linear Brody, Von Bertalanffy, Richards, Logistic and Gompertz models were used. To choose the best model, the Adjusted Coefficient of Determination, Convergence Rate, Residual Mean Square, Durbin-Watson Test, Akaike Information Criterion and Bayesian Information Criterion were applied as goodness-of-fit indicators. Cluster analysis was performed to check the similarity between models based on the mean parameter estimates. Among the studied models, Richards’ was the most suitable to describe the growth curves. The Logistic and Richards models were considered similar in the analysis with no distinction of lines as well as in the analyses of Lines 1, 2 and 3.
Highlights
The quail farming activity has become increasingly popular, as the species is a valuable protein source for humans (KHOSRAVI et al, 2016)
The growth curve can be altered through selection, i.e., by identifying animals with a faster growth rate without changing their adult weight rather than selecting increasingly large animals (SARMENTO et al, 2006)
The parameter estimates of the non-linear Logistic, Gompertz, Von Bertalanffy, Brody and Richards models (Table 3)
Summary
The quail farming activity has become increasingly popular, as the species is a valuable protein source for humans (KHOSRAVI et al, 2016). In a meat-type quail production system, weight-age variables are measured at pre-defined intervals, with the weight behavior analyzed over time. Non-linear models can be used to describe the growth of animals over time, making it possible to evaluate genetic and environmental factors that influence the growth curve. In this way, the growth curve can be altered through selection, i.e., by identifying animals with a faster growth rate without changing their adult weight rather than selecting increasingly large animals (SARMENTO et al, 2006)
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